# LLAMA: Leveraging Learning to Automatically Manage Algorithms

arXiv: https://arxiv.org/abs/1306.1031 · HTML: https://mlsanity.com/paper/1306.1031/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2013.

## Built on by (most cited first, 1 of 1)

- [Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection](https://mlsanity.com/paper/2608.17170/) (2026)

## Abstract (arXiv)

Algorithm portfolio and selection approaches have achieved remarkable improvements over single solvers. However, the implementation of such systems is often highly customised and specific to the problem domain. This makes it difficult for researchers to explore different techniques for their specific problems. We present LLAMA, a modular and extensible toolkit implemented as an R package that facilitates the exploration of a range of different portfolio techniques on any problem domain. It implements the algorithm selection approaches most commonly used in the literature and leverages the extensive library of machine learning algorithms and techniques in R. We describe the current capabilities and limitations of the toolkit and illustrate its usage on a set of example SAT problems.